Analyzing Frequently Mutated Genes and the Association With Tumor Mutation Load

Maruvka, Yosef E.; Haradhvala, Nicholas J.; Getz, Gad

doi:10.1001/jamaoncol.2019.0127

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Coincidentally, Muc16, which is frequently mutated and encodes the cancer antigen CA-125, was demonstrated to be associated with a higher TMB and favorable survival outcome of patients with gastric cancer (50). Moreover, Yosef found that in addition to Muc16, most (210 of 212) frequently mutated genes (defined as >1%) in gastric cancer were associated with a significant tumor mutation load (51). The observations in Muc16, which was the second-longest gene in the genome, also support the correlation between higher mutational burden and the mutated status of a gene with a long exon.…”

Section: Discussionmentioning

confidence: 99%

Titin mutation associated with responsiveness to checkpoint blockades in solid tumors

Jia¹,

Wang²,

He³

et al. 2019

JCI Insight

123

120

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Section: Discussionmentioning

confidence: 99%

Titin mutation associated with responsiveness to checkpoint blockades in solid tumors

Jia¹,

Wang²,

He³

et al. 2019

JCI Insight

123

120

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“…The undefined driving or accompanying role of DDR mutations in different pathologic types of PLC may also account for the different role of TMB in survival prognosis. More importantly, TMB may be the outcomes, not driving factors, from the oncogenic alterations which lead the poorer survival for some patients (45).…”

Section: Discussionmentioning

confidence: 99%

Alterations in DNA Damage Repair Genes in Primary Liver Cancer

Lin

Shi²,

Guo³

et al. 2019

Clinical Cancer Research

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Purpose: Alterations in DNA damage repair (DDR) genes produce therapeutic biomarkers. However, the characteristics and significance of DDR alterations remain undefined in primary liver cancer (PLC). Experimental Design: Patients diagnosed with PLC were enrolled in the trial (PTHBC, NCT02715089). Tumors and matched blood samples from participants were collected for a targeted next-generation sequencing assay containing exons of 450 cancer-related genes, including 31 DDR genes. The OncoKB knowledge database was used to identify and classify actionable alterations, and therapeutic regimens were determined after discussion by a multidisciplinary tumor board. Results: A total of 357 patients with PLC were enrolled, including 214 with hepatocellular carcinoma, 122 with ICC, and 21 with mixed hepatocellular-cholangiocarcinoma. A total of 92 (25.8%) patients had at least one DDR gene mutation, 15 of whom carried germline mutations. The most commonly altered DDR genes were ATM (5%) and BRCA1/2 (4.8%). The occurrence of DDR mutations was significantly correlated with a higher tumor mutation burden regardless of the PLC pathologic subtype. For DDR-mutated PLC, 26.1% (24/92) of patients possessed at least one actionable alteration, and the actionable frequency in DDR wild-type PLC was 18.9% (50/265). Eight patients with the BRCA mutation were treated by olaparib, and patients with BRCA2 germline truncation mutations showed an objective response. Conclusions: The landscape of DDR mutations and their association with genetic and clinicopathologic features demonstrated that patients with PLC with altered DDR genes may be rational candidates for precision oncology treatment.

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“…Meanwhile, the more frequent MUC16 mutation was associated with a higher TML (Li et al, 2018). Maruvka et al argued that a larger MUC16 mutation frequency could only be an accompanying result of high TML (Maruvka et al, 2019). MUC16 was also present in our 52-gene list.…”

Section: Discussionmentioning

confidence: 74%

A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features

et al. 2020

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Background: Stomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations. Methods: In this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association. Results: Typical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly. Conclusion: The study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.

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Analyzing Frequently Mutated Genes and the Association With Tumor Mutation Load

Abstract: 2. Mandelker D, Zhang L, Kemel Y, et al. Mutation detection in patients with advanced cancer by universal sequencing of cancer-related genes in tumor and normal dna vs guideline-based germline testing.

Cited by 6 publications

References 5 publications

Titin mutation associated with responsiveness to checkpoint blockades in solid tumors

Titin mutation associated with responsiveness to checkpoint blockades in solid tumors

Alterations in DNA Damage Repair Genes in Primary Liver Cancer

A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features

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